Deep learning-based transformation of H&E stained tissues into special stains

Kevin de Haan; Yijie Zhang; Jonathan E Zuckerman; Tairan Liu; Anthony E Sisk; Miguel F P Diaz; Kuang-Yu Jen; Alexander Nobori; Sofia Liou; Sarah Zhang; Rana Riahi; Yair Rivenson; W Dean Wallace; Aydogan Ozcan

doi:10.1038/s41467-021-25221-2

Deep learning-based transformation of H&E stained tissues into special stains

Nat Commun. 2021 Aug 12;12(1):4884. doi: 10.1038/s41467-021-25221-2.

Authors

Kevin de Haan^{1

2

3}, Yijie Zhang^{1

2

3}, Jonathan E Zuckerman⁴, Tairan Liu^{1

2

3}, Anthony E Sisk⁴, Miguel F P Diaz⁵, Kuang-Yu Jen⁶, Alexander Nobori⁴, Sofia Liou⁴, Sarah Zhang⁴, Rana Riahi⁴, Yair Rivenson^{7

8

9}, W Dean Wallace¹⁰, Aydogan Ozcan^{11

12

13

14}

Affiliations

¹ Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
² Bioengineering Department, University of California, Los Angeles, CA, USA.
³ California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA.
⁴ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁵ Kaiser Permanente Los Angeles Medical Center, Department of Pathology, Los Angeles, CA, USA.
⁶ Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, CA, USA.
⁷ Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA. rivensonyair@g.ucla.edu.
⁸ Bioengineering Department, University of California, Los Angeles, CA, USA. rivensonyair@g.ucla.edu.
⁹ California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA. rivensonyair@g.ucla.edu.
¹⁰ Department of Pathology and Laboratory Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA. william.wallace@med.usc.edu.
¹¹ Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA. ozcan@ucla.edu.
¹² Bioengineering Department, University of California, Los Angeles, CA, USA. ozcan@ucla.edu.
¹³ California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA. ozcan@ucla.edu.
¹⁴ Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA. ozcan@ucla.edu.

Abstract

Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson's Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Biopsy, Large-Core Needle / methods*
Coloring Agents / chemistry
Coloring Agents / classification
Coloring Agents / standards
Deep Learning*
Diagnosis, Computer-Assisted / methods*
Diagnosis, Differential
Humans
Kidney / pathology*
Kidney Diseases / diagnosis
Kidney Diseases / pathology*
Pathology, Clinical / methods
Pathology, Clinical / standards
Reference Standards
Reproducibility of Results
Sensitivity and Specificity
Staining and Labeling / methods*
Staining and Labeling / standards

Substances

Coloring Agents